import os

import jsonlines
import numpy as np
from tqdm import tqdm


_SCREAMING_SNAKE_CASE 		=      2_0_4_8
_SCREAMING_SNAKE_CASE 		=      4_0_9_6
_SCREAMING_SNAKE_CASE 		=      4_2
_SCREAMING_SNAKE_CASE 		=      os.environ.pop("""PROCESS_TRAIN""", """false""")
_SCREAMING_SNAKE_CASE 		=      {"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}





def 			lowercase(	UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 def choose_first(UpperCamelCase_							,					UpperCamelCase_=False       ):
  assert isinstance(UpperCamelCase_							,					UpperCamelCase_       )
  if len(UpperCamelCase_       ) == 1:
   UpperCamelCase			  =      answer[0]
   return {k: [answer[k]] for k in answer} if is_long_answer else answer
  for a in answer:
   if is_long_answer:
    UpperCamelCase			  =      {k: [a[k]] for k in a}
   if len(a["""start_token"""]       ) > 0:
    break
  return a

 UpperCamelCase			  =      {"""id""": example["""id"""]}
 UpperCamelCase			  =      example["""annotations"""]
 UpperCamelCase			  =      annotation["""yes_no_answer"""]
 if 0 in yes_no_answer or 1 in yes_no_answer:
  UpperCamelCase			  =      ["""yes"""] if 1 in yes_no_answer else ["""no"""]
  UpperCamelCase			  =      UpperCamelCase			  =      []
  UpperCamelCase			  =      UpperCamelCase			  =      []
  UpperCamelCase			  =      ["""<cls>"""]
 else:
  UpperCamelCase			  =      ["""short"""]
  UpperCamelCase			  =      choose_first(annotation["""short_answers"""]       )
  if len(out["""start_token"""]       ) == 0:
   # answer will be long if short is not available
   UpperCamelCase			  =      ["""long"""]
   UpperCamelCase			  =      choose_first(annotation["""long_answer"""]							,					is_long_answer=UpperCamelCase_       )
   UpperCamelCase			  =      []
  answer.update(UpperCamelCase_       )

 # disregard some samples
 if len(answer["""start_token"""]       ) > 1 or answer["start_token"] == answer["end_token"]:
  UpperCamelCase			  =      True
 else:
  UpperCamelCase			  =      False

 UpperCamelCase			  =      ["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
 if not all(isinstance(answer[k]							,					UpperCamelCase_       ) for k in cols       ):
  raise ValueError("""Issue in ID"""							,					example["""id"""]       )

 return answer





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=False       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      _get_single_answer(UpperCamelCase_       )
 # bytes are of no use
 del answer["start_byte"]
 del answer["end_byte"]

 # handle yes_no answers explicitly
 if answer["category"][0] in ["yes", "no"]:  # category is list with one element
  UpperCamelCase			  =      example["""document"""]["""tokens"""]
  UpperCamelCase			  =      []
  for i in range(len(doc["""token"""]       )       ):
   if not doc["is_html"][i]:
    context.append(doc["""token"""][i]       )
  return {
      "context": " ".join(UpperCamelCase_       ),
      "answer": {
          "start_token": -100,  # ignore index in cross-entropy
          "end_token": -100,  # ignore index in cross-entropy
          "category": answer["category"],
          "span": answer["category"],  # extra
      },
  }

 # later, help in removing all no answers
 if answer["start_token"] == [-1]:
  return {
      "context": "None",
      "answer": {
          "start_token": -1,
          "end_token": -1,
          "category": "null",
          "span": "None",  # extra
      },
  }

 # handling normal samples

 UpperCamelCase			  =      ["""start_token""", """end_token"""]
 answer.update({k: answer[k][0] if len(answer[k]       ) > 0 else answer[k] for k in cols}       )  # e.g. [10] == 10

 UpperCamelCase			  =      example["""document"""]["""tokens"""]
 UpperCamelCase			  =      answer["""start_token"""]
 UpperCamelCase			  =      answer["""end_token"""]

 UpperCamelCase			  =      []
 for i in range(len(doc["""token"""]       )       ):
  if not doc["is_html"][i]:
   context.append(doc["""token"""][i]       )
  else:
   if answer["start_token"] > i:
    start_token -= 1
   if answer["end_token"] > i:
    end_token -= 1
 UpperCamelCase			  =      """ """.join(context[start_token:end_token]       )

 # checking above code
 if assertion:
  UpperCamelCase			  =      doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
  UpperCamelCase			  =      doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
  UpperCamelCase			  =      """ """.join([old[i] for i in range(len(UpperCamelCase_       )       ) if not is_html[i]]       )
  if new != old:
   print("""ID:"""							,					example["""id"""]       )
   print("""New:"""							,					UpperCamelCase_							,					end="""\n"""       )
   print("""Old:"""							,					UpperCamelCase_							,					end="""\n\n"""       )

 return {
     "context": " ".join(UpperCamelCase_       ),
     "answer": {
         "start_token": start_token,
         "end_token": end_token - 1,  # this makes it inclusive
         "category": answer["category"],  # either long or short
         "span": new,  # extra
     },
 }





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=2048							,					UpperCamelCase_=4096							,					UpperCamelCase_=True       )		->	Optional[int]:







 '''simple docstring'''
 # overlap will be of doc_stride - q_len

 UpperCamelCase			  =      get_context_and_ans(UpperCamelCase_							,					assertion=UpperCamelCase_       )
 UpperCamelCase			  =      out["""answer"""]

 # later, removing these samples
 if answer["start_token"] == -1:
  return {
      "example_id": example["id"],
      "input_ids": [[-1]],
      "labels": {
          "start_token": [-1],
          "end_token": [-1],
          "category": ["null"],
      },
  }

 UpperCamelCase			  =      tokenizer(example["""question"""]["""text"""]							,					out["""context"""]       ).input_ids
 UpperCamelCase			  =      input_ids.index(tokenizer.sep_token_id       ) + 1

 # return yes/no
 if answer["category"][0] in ["yes", "no"]:  # category is list with one element
  UpperCamelCase			  =      []
  UpperCamelCase			  =      []
  UpperCamelCase			  =      input_ids[:q_len]
  UpperCamelCase			  =      range(UpperCamelCase_							,					len(UpperCamelCase_       )							,					max_length - doc_stride       )
  for i in doc_start_indices:
   UpperCamelCase			  =      i + max_length - q_len
   UpperCamelCase			  =      input_ids[i:end_index]
   inputs.append(q_indices + slice       )
   category.append(answer["""category"""][0]       )
   if slice[-1] == tokenizer.sep_token_id:
    break

  return {
      "example_id": example["id"],
      "input_ids": inputs,
      "labels": {
          "start_token": [-100] * len(UpperCamelCase_       ),
          "end_token": [-100] * len(UpperCamelCase_       ),
          "category": category,
      },
  }

 UpperCamelCase			  =      out["""context"""].split()
 UpperCamelCase			  =      splitted_context[answer["""end_token"""]]
 UpperCamelCase			  =      len(
     tokenizer(
         """ """.join(splitted_context[: answer["""start_token"""]]       )							,					add_special_tokens=UpperCamelCase_							,					).input_ids       )
 UpperCamelCase			  =      len(
     tokenizer(""" """.join(splitted_context[: answer["""end_token"""]]       )							,					add_special_tokens=UpperCamelCase_       ).input_ids       )

 answer["start_token"] += q_len
 answer["end_token"] += q_len

 # fixing end token
 UpperCamelCase			  =      len(tokenizer(UpperCamelCase_							,					add_special_tokens=UpperCamelCase_       ).input_ids       )
 if num_sub_tokens > 1:
  answer["end_token"] += num_sub_tokens - 1

 UpperCamelCase			  =      input_ids[answer["""start_token"""] : answer["""end_token"""] + 1]  # right & left are inclusive
 UpperCamelCase			  =      answer["""start_token"""]
 UpperCamelCase			  =      answer["""end_token"""]

 if assertion:
  UpperCamelCase			  =      tokenizer.decode(UpperCamelCase_       )
  if answer["span"] != new:
   print("""ISSUE IN TOKENIZATION"""       )
   print("""OLD:"""							,					answer["""span"""]       )
   print("""NEW:"""							,					UpperCamelCase_							,					end="""\n\n"""       )

 if len(UpperCamelCase_       ) <= max_length:
  return {
      "example_id": example["id"],
      "input_ids": [input_ids],
      "labels": {
          "start_token": [answer["start_token"]],
          "end_token": [answer["end_token"]],
          "category": answer["category"],
      },
  }

 UpperCamelCase			  =      input_ids[:q_len]
 UpperCamelCase			  =      range(UpperCamelCase_							,					len(UpperCamelCase_       )							,					max_length - doc_stride       )

 UpperCamelCase			  =      []
 UpperCamelCase			  =      []
 UpperCamelCase			  =      []
 UpperCamelCase			  =      []  # null, yes, no, long, short
 for i in doc_start_indices:
  UpperCamelCase			  =      i + max_length - q_len
  UpperCamelCase			  =      input_ids[i:end_index]
  inputs.append(q_indices + slice       )
  assert len(inputs[-1]       ) <= max_length, "Issue in truncating length"

  if start_token >= i and end_token <= end_index - 1:
   UpperCamelCase			  =      start_token - i + q_len
   UpperCamelCase			  =      end_token - i + q_len
   answers_category.append(answer["""category"""][0]       )  # ["short"] -> "short"
  else:
   UpperCamelCase			  =      -100
   UpperCamelCase			  =      -100
   answers_category.append("""null"""       )
  UpperCamelCase			  =      inputs[-1][start_token : end_token + 1]

  answers_start_token.append(UpperCamelCase_       )
  answers_end_token.append(UpperCamelCase_       )
  if assertion:
   if new != old and new != [tokenizer.cls_token_id]:
    print("""ISSUE in strided for ID:"""							,					example["""id"""]       )
    print("""New:"""							,					tokenizer.decode(UpperCamelCase_       )       )
    print("""Old:"""							,					tokenizer.decode(UpperCamelCase_       )							,					end="""\n\n"""       )
  if slice[-1] == tokenizer.sep_token_id:
   break

 return {
     "example_id": example["id"],
     "input_ids": inputs,
     "labels": {
         "start_token": answers_start_token,
         "end_token": answers_end_token,
         "category": answers_category,
     },
 }





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=2048							,					UpperCamelCase_=4096							,					UpperCamelCase_=False       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      get_strided_contexts_and_ans(
     UpperCamelCase_							,					UpperCamelCase_							,					doc_stride=UpperCamelCase_							,					max_length=UpperCamelCase_							,					assertion=UpperCamelCase_							,					)

 return example





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 with jsonlines.open(UpperCamelCase_							,					"""a"""       ) as writer:
  for example in tqdm(UpperCamelCase_							,					total=len(UpperCamelCase_       )							,					desc="""Saving samples ... """       ):
   UpperCamelCase			  =      example["""labels"""]
   for ids, start, end, cat in zip(
       example["""input_ids"""]							,					labels["""start_token"""]							,					labels["""end_token"""]							,					labels["""category"""]							,					):
    if start == -1 and end == -1:
     continue  # leave waste samples with no answer
    if cat == "null" and np.random.rand() < 0.6:
     continue  # removing 50 % samples
    writer.write(
        {
            """input_ids""": ids,
            """start_token""": start,
            """end_token""": end,
            """category""": CATEGORY_MAPPING[cat],
        }       )


if __name__ == "__main__":
     from datasets import load_dataset

     from transformers import BigBirdTokenizer

     _SCREAMING_SNAKE_CASE 		=      load_dataset("""natural_questions""")
     _SCREAMING_SNAKE_CASE 		=      BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

     _SCREAMING_SNAKE_CASE 		=      data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]

     _SCREAMING_SNAKE_CASE 		=      {
         """tokenizer""": tokenizer,
         """doc_stride""": DOC_STRIDE,
         """max_length""": MAX_LENGTH,
         """assertion""": False,
     }
     _SCREAMING_SNAKE_CASE 		=      data.map(prepare_inputs, fn_kwargs=fn_kwargs)
     _SCREAMING_SNAKE_CASE 		=      data.remove_columns(["""annotations""", """document""", """id""", """question"""])
     print(data)

     np.random.seed(SEED)
     _SCREAMING_SNAKE_CASE 		=      """nq-training.jsonl""" if PROCESS_TRAIN == """true""" else """nq-validation.jsonl"""
     save_to_disk(data, file_name=cache_file_name)

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase		):







   @register_to_config
   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						int = 128   ,   lowerCamelCase_						:						int = 256   ,   lowerCamelCase_						:						float = 2_0_0_0.0   ,   lowerCamelCase_						:						int = 768   ,   lowerCamelCase_						:						int = 12   ,   lowerCamelCase_						:						int = 12   ,   lowerCamelCase_						:						int = 64   ,   lowerCamelCase_						:						int = 2048   ,   lowerCamelCase_						:						float = 0.1   ,   ):





    """simple docstring"""


    super().__init__()

    UpperCamelCase			  =      nn.Sequential(
        nn.Linear(lowerCamelCase_   ,   d_model * 4   ,   bias=lowerCamelCase_				)   ,   nn.SiLU()   ,   nn.Linear(d_model * 4   ,   d_model * 4   ,   bias=lowerCamelCase_				)   ,   nn.SiLU()   ,   )

    UpperCamelCase			  =      nn.Embedding(lowerCamelCase_   ,   lowerCamelCase_				)
    UpperCamelCase			  =      False

    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)

    UpperCamelCase			  =      nn.Dropout(p=lowerCamelCase_				)

    UpperCamelCase			  =      nn.ModuleList()
    for lyr_num in range(lowerCamelCase_				):
     # FiLM conditional T5 decoder
     UpperCamelCase			  =      DecoderLayer(d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)
     self.decoders.append(lowerCamelCase_				)

    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_				)

    UpperCamelCase			  =      nn.Dropout(p=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      torch.mul(query_input.unsqueeze(-1				)   ,   key_input.unsqueeze(-2				)				)
    return mask.unsqueeze(-3				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      decoder_input_tokens.shape
    assert decoder_noise_time.shape == (batch,)

    # decoder_noise_time is in [0, 1), so rescale to expected timing range.
    UpperCamelCase			  =      get_timestep_embedding(
        decoder_noise_time * self.config.max_decoder_noise_time   ,   embedding_dim=self.config.d_model   ,   max_period=self.config.max_decoder_noise_time   ,   ).to(dtype=self.dtype				)

    UpperCamelCase			  =      self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

    assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

    UpperCamelCase			  =      decoder_input_tokens.shape[1]

    # If we want to use relative positions for audio context, we can just offset
    # this sequence by the length of encodings_and_masks.
    UpperCamelCase			  =      torch.broadcast_to(
        torch.arange(lowerCamelCase_   ,   device=decoder_input_tokens.device				)   ,   (batch, seq_length)   ,   )

    UpperCamelCase			  =      self.position_encoding(lowerCamelCase_				)

    UpperCamelCase			  =      self.continuous_inputs_projection(lowerCamelCase_				)
    inputs += position_encodings
    UpperCamelCase			  =      self.dropout(lowerCamelCase_				)

    # decoder: No padding present.
    UpperCamelCase			  =      torch.ones(
        decoder_input_tokens.shape[:2]   ,   device=decoder_input_tokens.device   ,   dtype=inputs.dtype				)

    # Translate encoding masks to encoder-decoder masks.
    UpperCamelCase			  =      [(x, self.encoder_decoder_mask(lowerCamelCase_   ,   lowerCamelCase_				)) for x, y in encodings_and_masks]

    # cross attend style: concat encodings
    UpperCamelCase			  =      torch.cat([x[0] for x in encodings_and_encdec_masks]   ,   dim=1				)
    UpperCamelCase			  =      torch.cat([x[1] for x in encodings_and_encdec_masks]   ,   dim=-1				)

    for lyr in self.decoders:
     UpperCamelCase			  =      lyr(
         lowerCamelCase_   ,   conditioning_emb=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   encoder_attention_mask=lowerCamelCase_   ,   )[0]

    UpperCamelCase			  =      self.decoder_norm(lowerCamelCase_				)
    UpperCamelCase			  =      self.post_dropout(lowerCamelCase_				)

    UpperCamelCase			  =      self.spec_out(lowerCamelCase_				)
    return spec_out





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						List[str]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Any=1E-6				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.ModuleList()

    # cond self attention: layer 0
    self.layer.append(
        TaLayerSelfAttentionCond(d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)				)

    # cross attention: layer 1
    self.layer.append(
        TaLayerCrossAttention(
            d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_   ,   layer_norm_epsilon=lowerCamelCase_   ,   )				)

    # Film Cond MLP + dropout: last layer
    self.layer.append(
        TaLayerFFCond(d_model=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_   ,   layer_norm_epsilon=lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[Any]=None   ,   lowerCamelCase_						:						int=None   ,   lowerCamelCase_						:						Optional[int]=None   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer[0](
        lowerCamelCase_   ,   conditioning_emb=lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   )

    if encoder_hidden_states is not None:
     UpperCamelCase			  =      torch.where(encoder_attention_mask > 0   ,   0   ,   -1E10				).to(
         encoder_hidden_states.dtype				)

     UpperCamelCase			  =      self.layer[1](
         lowerCamelCase_   ,   key_value_states=lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   )

    # Apply Film Conditional Feed Forward layer
    UpperCamelCase			  =      self.layer[-1](lowerCamelCase_   ,   lowerCamelCase_				)

    return (hidden_states,)





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_				)
    UpperCamelCase			  =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowerCamelCase_				)
    UpperCamelCase			  =      Attention(query_dim=lowerCamelCase_   ,   heads=lowerCamelCase_   ,   dim_head=lowerCamelCase_   ,   out_bias=lowerCamelCase_   ,   scale_qk=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Tuple=None   ,   lowerCamelCase_						:						Optional[Any]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)

    if conditioning_emb is not None:
     UpperCamelCase			  =      self.FiLMLayer(lowerCamelCase_   ,   lowerCamelCase_				)

    # Self-attention block
    UpperCamelCase			  =      self.attention(lowerCamelCase_				)

    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)

    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      Attention(query_dim=lowerCamelCase_   ,   heads=lowerCamelCase_   ,   dim_head=lowerCamelCase_   ,   out_bias=lowerCamelCase_   ,   scale_qk=lowerCamelCase_				)
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_   ,   eps=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Dict=None   ,   lowerCamelCase_						:						int=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)
    UpperCamelCase			  =      self.attention(
        lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   attention_mask=attention_mask.squeeze(1				)   ,   )
    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)
    return layer_output





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      TaDenseGatedActDense(d_model=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)
    UpperCamelCase			  =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowerCamelCase_				)
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_   ,   eps=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						int=None				):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)
    if conditioning_emb is not None:
     UpperCamelCase			  =      self.film(lowerCamelCase_   ,   lowerCamelCase_				)

    UpperCamelCase			  =      self.DenseReluDense(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)
    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)
    UpperCamelCase			  =      NewGELUActivation()







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.act(self.wi_a(lowerCamelCase_				)				)
    UpperCamelCase			  =      self.wi_a(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_gelu * hidden_linear
    UpperCamelCase			  =      self.dropout(lowerCamelCase_				)

    UpperCamelCase			  =      self.wo(lowerCamelCase_				)
    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Optional[int]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Dict=1E-6				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Parameter(torch.ones(lowerCamelCase_				)				)
    UpperCamelCase			  =      eps







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      hidden_states.to(torch.floataa				).pow(2				).mean(-1   ,   keepdim=lowerCamelCase_				)
    UpperCamelCase			  =      hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

    # convert into half-precision if necessary
    if self.weight.dtype in [torch.floataa, torch.bfloataa]:
     UpperCamelCase			  =      hidden_states.to(self.weight.dtype				)

    return self.weight * hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						torch.Tensor				):





    """simple docstring"""


    return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.0_4_4_7_1_5 * torch.pow(lowerCamelCase_   ,   3.0				))				))





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   out_features * 2   ,   bias=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.scale_bias(lowerCamelCase_				)
    UpperCamelCase  ,     UpperCamelCase			  =      torch.chunk(lowerCamelCase_   ,   2   ,   -1				)
    UpperCamelCase			  =      x * (1 + scale) + shift
    return x
